This project achieved three key goals: pioneering research on movement interactions and polarization, developing an algorithm for identifying divisive issues, and advancing computational social science (CSS) in political science.
To address Objective 1, I researched the intricate dynamics of movement interactions and polarization. By compiling and analyzing two big Twitter datasets encompassing the #BlackLivesMatter and #MeToo movements, I gained insights into how online discussions unfold around crucial topics, revealing issues of support, opposition, and interconnectivity. This work, outlined in 6 co-authored papers (one published, five under review) and presented at academic conferences and online talks, sheds new light on polarization and social justice in democracies.
Concerning Objective 2, I developed an algorithm to analyze social media data and identify the key themes that structure online debates around divisive issues. This algorithm was incorporated into the Dia-Pol website (
https://research.diapols.com/(opens in new window)) to provide open access to these insights. This user-friendly platform, featuring dynamic visualizations and comparative analysis tools, has become a valuable resource for researchers and the public alike, fostering transparency and raising awareness about critical social movements. To the best of my knowledge, it is the first website that summarizes the main topics of MT and BLM.
Regarding objective 3, I dedicated significant efforts to advancing CSS education and research. Through a series of international training sessions and workshops, including those at the American Political Science Association and the Summer Institutes in Computational Social Science, I trained about 122 researchers with practical skills in machine learning, natural language processing, and network analysis. In Turkey, I offered training at Tevitol, a high school for gifted students on Jun. 12, 2022. At Bogazici, I established a CSS lab and trained 55 students in machine learning, natural language processing, and network analysis. Our students have secured prestigious doctoral positions at top universities like Northwestern, Stanford, and Oxford, while others have embarked on successful careers in data science. Moreover, I offered two data science courses at the political science department: POLS 204 Advanced Statistics and POLS 598 Research & Reading in analyzing social media data. About 44 students took these classes. Three students wrote dissertations using CSS methods, while three others landed prestigious doctoral positions at Northwestern, Stanford, and Oxford thanks to their CSS skills. Relatedly, I published a book chapter on incorporating computational social science in Political science in an edited book entitled Opportunities and Challenges for Computational Social Science Methods (
https://orcid.org/0000-0001-5479-4801(opens in new window)).